C-VeT: UCLA Vehicular Testbed Pis: Mario Gerla (UCLA) - - PowerPoint PPT Presentation

http://netelab.cs.ucla.edu/

http://vehicular.cs.ucla.edu/

C-VeT: UCLA Vehicular Testbed

Pis: Mario Gerla (UCLA) gerla@cs.ucla.edu Giovanni Pau (UCLA) gpau@cs.ucla.edu collaborative work with Suzanne Pauslon (UCLA Atmospheric Department) Eugenio Giordano (UCLA/University of Bologna/MSR PhD Fellow) Gustavo Marfia (UCLA) Consultant: Christian Benvenuti (Cisco Systems) Daniel Jiang (Daimler) Luca Delgrossi (Daimler) Antony Rowstron (Microsoft Research) gpau@cs.ucla.edu http://www.cs.ucla.edu/~gpau

Goals

 Provide:

 A “Planet Lab Inspired” platform to support car-to-car experiments in

various traffic conditions and mobility patterns

 A shared virtualized environment to test new protocols and

applications

 Full Virtualization Through Xen (featuring a shared testbed)  MadWiFi Virtualization (with on demand exclusive use)  Large Scale Experiments through Emulator.

 Allow:

 Collection of mobility traces and network statistics  Experiments on a real vehicular network  Provide a platform for Urban Sensing and Intelligent Transportation

Systems

 Deployment of innovative V2V V2I applications in several areas

including Info-mobility, Environmental Monitoring, Infotainment, and Homeland Defense application

Why a Campus?

 Campus Environment Very similar to a small city

 Police Cars  Busses  Service Cars  Private Cars  Urban Scenario (Low Raise/High Raise buildings)  Street Lights  Stop Signals  ....

 Additional Features:

 Campus Mesh Coverage  Full control of the network  Fully customizable platforms

Why C-VeT is different from Others?

 C-VeT Vehicles are CAMPUS operated, on the

road more than 16 hour very day.



C-VeT will have:



30 Campus operated vehicles (including bus and facility management vehicles).



Phase 1: 8 Facility Management + 4 Housing Vehicles



Exploitation of “on a schedule” and “random” campus fleet mobility patterns



Systemic Trace and pollution data Collection:



30 Commuting Vans



Measure freeway motion patterns (only tracking equipment installed in this fleet).



Opportunistic Network



Ad Hoc,



Mesh Infrastructure



Wimax



3G



Integration of C-VeT with a Data Center Emulator for larger scale experiments ~200 emulated Nodes.

The Technology

 Wireless Connectivity

 DSRC Radio access  IEEE 802.11 a/b/g/n with MadWiFi  Open Platform Programmable MIMO Radios  WiMax Coverage

 Sensing Platform

 Environmental:

 CO2, NOX, SO2, PM2.5 and Black Carbon sensors.  Traffic Light as actuators

 In Vehicle sensing

 OBD2/CANBUS interface  Driver Behavior  Navigators

Black Carbon Meter

Sample Equipment

 Environmental Monitoring Applications

Communication Engine CO2/Temperature/Umidity sensors Particle Sensors NOX/Ozone Sensors Cameras

 Three class of nodes:

 Actual nodes installed in Vehicles  Cappucino PC-Platform with several instances of the C-

VeT Virtual Machine running on the actual Facility vehicles.

 Emulated Nodes Installed in the Laboratory

 Instances of the C-VeT virtual Machine sitting in our Lab

computing cluster. For the user those nodes will appear as regular vehicles.  Write code ONLY ONCE.

 The OSI Layers 3 to 6 will be the same as in the C-VeT VM

installed in the actual facility vehicles.

Testbed Sharing:

 Ok, UCLA is building a Testbed but why should I

care?

 Access to the C-VeT testbed will be granted through web

interface and remote shell

 Web interface for user registration and management

 How I will be able to use, RTT could kill my

development:

 Downloadable virtual appliance and SDK for a

streamlined software development.

 Web Streamlined experiment deployment and monitoring.  Remote access to C-VeT node’s VMs through web

interface or VNC

Campus Mesh Overview

• Phase I: Coverage of South Campus.
• Pase II: Full Campus Coverage

Campus Mesh Overview

http://netelab.cs.ucla.edu/

http://vehicular.cs.ucla.edu/

Now we have a testbed and so What?

Research topics

 Info Mobility  Urban Sensing

 Environmental Protection  National Security

 Routing  Network Security  Cross Layer optimization  Propagation Experiments  Mobility management/Location service (virtualizable

Overlay service - Planet lab inspired)

 Road safety

Importance of System Integration

 Collaboration with City Authorities to deploy

incentives for “Greener” cars:

 Easier Access to City Centers  Access to Taxi-Lanes/Car-Pool Lanes  Discounted access for lower impact vehicles

 Intelligent Traffic Signals

 Changes in the traffic flow  Changes in the timing  Tools

FBK - June 09

Importance of Mobility

 Classical Models Fail to

describe reality:

 Random Way Point  Constraint Random Way Point  ..

 Los Alamos Portland Traces

showed the importance of Accurate models

 5X7Km downtown Portland  16,6000 Cars  1 second granularity traces  Mobility model derived from:

 Extensive Census data  Custom Survey on Portland

Citizens' activities

 Cellular Automata based micro-

mobility simulations.

Info Mobility Simulation

 Mobility  Portland Model

 Vehicles every 30 seconds broadcast they average

speed and location for the last 30 seconds

 The information is replicated by others vehicle with a

probability inverse to the distance from the source

Information Delay

UCLA TR-09008,

Urban Sensor Grid

 Applications

 Pollution Tracking  Treat Tracking  Illegal Dumping tracking  Pollution Monitoring  Traffic Management

 Vision:

 Government Vehicles can be equipped with an extensive

sensor platform to monitor the environment and report to the

control center for tracking and management purposes.

 Challenges:

 Protocols and models for Information Delivery  Cooperative sensing Intelligence (i.e. a treat is a treat and when is a

false alarm?)

 Distributed actuators (what I do when a problem is detected?)

Pollution: Where Vehicle Stand?

 Transportation Accounts for 28% of the TOTAL GREEN

HOUSE GASES … RAISING …

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ITC to support GHG Reduction …

 Reduce congestion, acceleration-deceleration

 In Car Navigators are our Probes in the system  Communication uses to gather the information  Communication used to push the optimized Navigation path

 Challenges in Path Optimization and Scalability  Cellular Choice in General but in some cases Opportunistic Networks

could save the day.

 Current technology based on in asphalt sensor too expensive

to scale $100K for installation and $15K/year in Maintenance per intersection.

 Some advanced examples: Google Navigation  Cyber Physical Interactions are the Key.

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… ITC to support GHG Reduction …

 ITS to reduce poor signal timing could reduce 1.315

MMT CO2/yr

 Current Timing computed using Magnetic Spires, NO or

Limited coordination between different traffic lights.

 Time optimization is essential in reducing Idle but  NON

LINEAR multi-dimensional Optimization problem!

 Cars as Sensors could gather real-time information on traffic

level and with the cost of an Access Point enable many traffic lights to become “smart”

 Close Loop control between traffic sensing and Traffic

Signals (lights and intelligent Signs) could lead to substantial emission and exposure reduction. Cars are the SENSORS intelligent traffic signs the ACTUATORS

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Data: DOT/EPA 2007

… ITC to support GHG Reduction …

 Reduce idling and encourage “eco driving” by

drivers

 Including the Vehicle in the Control Loop (OBD2 and

beyond)

 HIGH RESISTANCE FROM AUTOMAKERS

 High pay-off potential

 MBZ developed a “closed” system to keep vehicles at certain

distance from follower and previous

 20% GAS SAVINGS just with a RADAR.

 Honda can turn off part of the engine when is not needed (i.e.

traffic light idle).

 What if Traffic lights announce their status?

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http://netelab.cs.ucla.edu/

http://vehicular.cs.ucla.edu/

Pollution Management and Terror Prevention A Common Approach!

A Concept Demo in Simulation

The Campus Vehicular Testbed will allow us to try this with pollution sensors

Some Challenges

 Propagation Models

 Corner Model – WONS 2009.

 Intermittent Connectivity

 UCLA TR-090017 – Characteristics of Vehicular Networks.

 Shows and quantifies intermittent connectivity on Portland Traces .

 Routing Protocols

 PVRP Practical Vehicular Routing Protocol – Mobicom `09 work in

progress paper.

 A Disruption Tolerant Discovery/Routing protocols that exploits Maps

 Data Mining and Analysis

 GPS data extraction for Maps – Mobisys 09 Submission  GPS extraction for Traffic management

 Close Loop Optimization with Actuators  Initial on-the-road experiments.

 VTC 08, ViVec 08, Mobisys 07 poster session.

Few Preliminary Experiments

 Initial Experiment in

2008.

 4 Vehicles  OLSR Routing  Video Streaming

application

 This is the routing

map.

http://netelab.cs.ucla.edu/

http://vehicular.cs.ucla.edu/

THANKS